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Forecasting of AQI (PM2.5) for the three most polluted cities in India during COVID-19 by hybrid Daubechies discrete wavelet decomposition and autoregressive (Db-DWD-ARIMA) model

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Abstract

Air pollution has emerged as a significant environmental challenge at the global level, and India is majorly affected by it. Numerous emission sources, such as automobiles, industries, fuel-burning for household and commercial activities, and dust due to construction activities, are responsible for air pollution. The lockdown in India which was clamped for controlling the spread of virulent disease also brought down the level of pollutants in air significantly. The proposed approach deals with the application of the hybrid model of Daubechies discrete wavelet decomposition (Db-DWD) and the autoregressive integrated moving average (ARIMA) model for modeling and forecasting the chaotic data of air quality index (PM2.5) from the three most polluted cities (Agra, New Delhi, and Varanasi) in India for pre and within lockdown periods. The estimated outputs of the component series are then reconstructed to obtain the final forecast of the AQI data. The statistical evaluation compares the performance of the simple ARIMA model and the joint Db-DWD-ARIMA model. Also, the coupled model has been applied for forecasting efficacy with Daubechies mother wavelet of orders 5, 8, 10, and 12. The hybrid model reduced forecasting errors and improved accuracy significantly. Secondly, the forecasting efficiencies in this hybrid model have enhanced with the increase in wavelet order. This study will help to assess and take appropriate steps to control air pollution levels and to monitor the growing air pollutants, which will be significant for our existence.

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Data availability

Data is freely available at https://cpcb.nic.in/ website.

Abbreviations

PM2.5 :

Particulate matter less than 5 microns

PM10 :

Particulate matter less than 10 microns

AQI:

Air quality index

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Acknowledgements

The authors thank the Central Pollution Control Board, the Ministry of Environment, Forest and Climate Change, Government of India for the research data. The authors also thank I K Gujral Punjab Technical University, Government of Punjab, for the research facilities.

Funding

The corresponding author thanks SERB-DST, Ministry of Science and Technology, Government of India, for providing the research project grant (MTR/2020/000479).

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Authors and Affiliations

  1. Department of Mathematics, Guru Nanak Dev University College, Verka, Amritsar, Punjab, 143501, India

    Jatinder Kaur

  2. Department of Mathematical Sciences, I.K. Gujral Punjab Technical University, Jalandhar, Punjab, 144603, India

    Jatinder Kaur & Kulwinder Singh Parmar

  3. Department of Mathematics, Guru Nanak Dev University College, Narot Jaimal Singh, Pathankot, Punjab, 145026, India

    Sarbjit Singh

Authors
  1. Jatinder Kaur
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  3. Kulwinder Singh Parmar
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Contributions

All authors have equally contribution in the manuscript. Corresponding author (Kulwinder Singh Parmar) and Sarabjit Singh contribute to develop concept and help in modeling part. First author (Jatinder Kaur) writes the manuscript and analyze the data.

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Correspondence to Kulwinder Singh Parmar.

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Kaur, ., Singh, S. & Parmar, K.S. Forecasting of AQI (PM2.5) for the three most polluted cities in India during COVID-19 by hybrid Daubechies discrete wavelet decomposition and autoregressive (Db-DWD-ARIMA) model. Environ Sci Pollut Res 30, 101035–101052 (2023). https://doi.org/10.1007/s11356-023-29501-w

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